scholarly journals SVM versus MAP on Accelerometer Data to Distinguish among Locomotor Activities Executed at Different Speeds

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Maurizio Schmid ◽  
Francesco Riganti-Fulginei ◽  
Ivan Bernabucci ◽  
Antonino Laudani ◽  
Daniele Bibbo ◽  
...  
Keyword(s):  
Inertial Sensor ◽  
Stair Climbing ◽  
Support Vector ◽  
Accelerometer Data ◽  
Sammon’S Mapping ◽  
Locomotor Activities ◽  
Artificial Neural Network Ann ◽  
Better Than ◽  
Bayes Approach

Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes’ classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25–35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon’s mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon’s mapping on the whole dataset. In the Bayes’ approach, the two features were then fed to a Bayes’ classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes’ approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes’ scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

Automatic Classification of ECG Using Multi-Classifiers with a New Fusion Method

2014 ◽  
Vol 687-691 ◽  
pp. 3917-3922
Author(s):  
Yi Chang Wang ◽  
Feng Qi Yan ◽  
Yu Fang
Keyword(s):  
Traditional Method ◽  
Heart Beat ◽  
Vector Model ◽  
Support Vector ◽  
Beat Classification ◽  
Computer Aided Analysis ◽  
Important Basis ◽  
Voting Method ◽  
Better Than

ECG signal contains abundant information of human heart activity. It is important basis of doctors’ diagnose. With the development of computer technology, computer aided analysis has been widely applied in the field of ECG analysis. Most of the traditional method is based on single classifier and too complex. Also, the accuracy is not high. This paper focuses on ECG heart beat classification, extracting different types of feature, training different classifiers by vector model and support vector machine (SVM), merging the result of multiple classifiers. In this paper, we used the advanced voting method (voting by weight) to fusion the result of different classifier, having compared it with the traditional voting method.It performed better than traditional method in term of accuracy

scholarly journals Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

2017 ◽  
Vol 9 (4) ◽  
pp. 416 ◽  
Author(s):  
Nelly Indriani Widiastuti ◽  
Ednawati Rainarli ◽  
Kania Evita Dewi
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Support Vector ◽  
Good Reputation ◽  
Multiclass Support Vector Machine ◽  
Simple Logistic ◽  
Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

A Novel Approach for Dimensionality Reduction and Classification of Big Data Using Meta-Heuristic Approach

2020 ◽  
Vol 17 (11) ◽  
pp. 5182-5197
Author(s):  
Amrinder Kaur ◽  
Rakesh Kumar
Keyword(s):  
Big Data ◽  
User Interaction ◽  
Extraction Process ◽  
Support Vector ◽  
Novel Approach ◽  
Whale Optimization ◽  
Distribution Index ◽  
Artificial Neural Network Ann ◽  
Feature Selection Approach

User interaction over the internet is growing day by day. The social network users send massive information to the network to share with others on the network. This increases the information on social media, hence needed a mechanism to handle or manage such high dimensional data termed as Big Data. Big Data reduction can be performed by using a feature selection approach. But, the Classification of such massive data is a challenging task for all the researchers. To overcome this problem, a metaheuristic based Genetic Algorithm (GA) for the selection of most suitable rows which can be provided for training. The selected rows undergo a feature extraction process, which is attained by Principle Component Analysis (PCA). The extracted principle components are optimized using another meta-heuristic algorithm termed as Whale Optimization. As the proposed algorithm uses unlabelled data, clustering is done to label the data. Two different distribution indexes were calculated for data with GA selected rows and data with GA selected rows along with PCA and whale. The distribution index is the ratio of a total number of elements in one cluster to a total number of elements in the second cluster. High distribution index leads to better accuracy when it comes to classifying the text data. The data is clustered using the K-Means algorithm to find the cluster indexes. The proposed algorithm presents a hybrid classification mechanism with upper and lower boundaries of classified labels using Artificial Neural Network (ANN) and Support Vector Machine (SVM).

MACHINE LEARNING ALGORITHMS FOR IDENTIFICATION OF ABNORMAL GLOW CURVES AND ASSOCIATED ABNORMALITY IN CaSO4:DY-BASED PERSONNEL MONITORING DOSIMETERS

2020 ◽  
Vol 190 (3) ◽  
pp. 342-351
Author(s):  
Munir S Pathan ◽  
S M Pradhan ◽  
T Palani Selvam
Keyword(s):  
Machine Learning ◽  
Glow Curve ◽  
Good Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Computationally Efficient ◽  
Artificial Neural Network Ann ◽  
First Time

Abstract In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorithms. The performance of all algorithms is compared by using various parameters. Results show a fairly good accuracy of 99.05% for the classification of GCs by RF algorithm. Whereas 96.7% and 96.1% accuracy is achieved using ANN and SVM, respectively. The RF-based classifier is recommended for GC classification as well as in assisting the fault determination of the TLD reader system.

scholarly journals Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology

Micromachines ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 411 ◽  
Author(s):  
Jae-Neung Lee ◽  
Yeong-Hyeon Byeon ◽  
Keun-Chang Kwak
Keyword(s):  
Mean Squared Error ◽  
Inertial Sensor ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Sensor Technology ◽  
Support Vector ◽  
Softmax Classifier ◽  
National Team ◽  
Motion Data

This paper discusses the classification of horse gaits for self-coaching using an ensemble stacked auto-encoder (ESAE) based on wavelet packets from the motion data of the horse rider. For this purpose, we built an ESAE and used probability values at the end of the softmax classifier. First, we initialized variables such as hidden nodes, weight, and max epoch using the options of the auto-encoder (AE). Second, the ESAE model is trained by feedforward, back propagation, and gradient calculation. Next, the parameters are updated by a gradient descent mechanism as new parameters. Finally, once the error value is satisfied, the algorithm terminates. The experiments were performed to classify horse gaits for self-coaching. We constructed the motion data of a horse rider. For the experiment, an expert horse rider of the national team wore a suit containing 16 inertial sensors based on a wireless network. To improve and quantify the performance of the classification, we used three methods (wavelet packet, statistical value, and ensemble model), as well as cross entropy with mean squared error. The experimental results revealed that the proposed method showed good performance when compared with conventional algorithms such as the support vector machine (SVM).

Brazilian Coins Recognition Using Histogram of Oriented Gradients Features

2019 ◽  
Vol 16 (10) ◽  
pp. 4170-4178
Author(s):  
Sheifali Gupta ◽  
Gurleen Kaur ◽  
Deepali Gupta ◽  
Udit Jindal
Keyword(s):  
Region Of Interest ◽  
Initial Step ◽  
Support Vector ◽  
Histogram Of Oriented Gradients ◽  
Lighting Conditions ◽  
Roi Extraction ◽  
Feature Values ◽  
Artificial Neural Network Ann ◽  
Ann Classifier

This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.

DIALOG ACT CLASSIFICATION USING ACOUSTIC AND DISCOURSE INFORMATION OF MAPTASK DATA

2010 ◽  
Vol 09 (04) ◽  
pp. 289-311 ◽  
Author(s):  
FATEMA N. JULIA ◽  
KHAN M. IFTEKHARUDDIN ◽  
ATIQ U. ISLAM
Keyword(s):  
Classifier Fusion ◽  
Support Vector ◽  
Acoustic Features ◽  
Average Precision ◽  
Data Set ◽  
Parts Of Speech ◽  
Pos Tagging ◽  
Accuracy Rates ◽  
Better Than

Dialog act (DA) classification is useful to understand the intentions of a human speaker. An effective classification of DA can be exploited for realistic implementation of expert systems. In this work, we investigate DA classification using both acoustic and discourse information for HCRC MapTask data. We extract several different acoustic features and exploit these features using a Hidden Markov Model (HMM) network to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique that effectively reduces the dimensionality of discourse features. To classify discourse information, we exploit two classifiers such as a HMM and Support Vector Machine (SVM). We further obtain classifier fusion between HMM and SVM to improve discourse classification. Finally, we perform an efficient decision-level classifier fusion for both acoustic and discourse information to classify 12 different DAs in MapTask data. We obtain 65.2% and 55.4% DA classification rates using acoustic and discourse information, respectively. Furthermore, we obtain combined accuracy of 68.6% for DA classification using both acoustic and discourse information. These accuracy rates of DA classification are either comparable or better than previously reported results for the same data set. For average precision and recall, we obtain accuracy rates of 74.89% and 69.83%, respectively. Therefore, we obtain much better precision and recall rates for most of the classified DAs when compared to existing works on the same HCRC MapTask data set.

scholarly journals Identification of Plant Leaf Disease using Machine Learning Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 6077-6081 ◽  
Keyword(s):  
Plant Disease ◽  
Digital Camera ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Leaf Sample ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Identification Techniques ◽  
Processing Techniques ◽  
Better Than

Plant disease identification and classification is major area of research as majority of people in India depend on agriculture for their main source of income and for food. Identification of the diseases in any crops is challenging since manual identification techniques being used in this are based on the experts advises which may not be efficient. Based on leaf features decisions about variety of diseases are taken. In this paper an automated framework is introduced which can be used to detect and classify the diseases in the leaf accurately. Leaf images are acquired by using digital camera. Pre-processing techniques, segmentation and feature extraction are performed on the acquired images. The features are passed on to the classifiers to classify the diseases. This work has been proposed to classify and distinguish the leaf sample based on its features. The proposed work is carried out with Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naive Bayes classifiers to analyze the result. For given dataset ANN performed better than the other two classifiers

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